THE METHOD OF MUTUAL SPECTRA IN UAV NOISE DETECTION PROBLEMS

Abstract

The article is devoted to the relevant direction of acoustic detection of UAVs, in connection with the increasing demands on aerial surveillance and reconnaissance systems. The physical fields that can be detected by a UAV detection device are very diverse: electromagnetic, optical, and acoustic. In turn, the acoustic field is characterized by a band of noise-like signals that can be interpreted as useful and interfering. Moreover, a wide range of UAVs is not always subject to detection. Therefore, it would be desirable to obtain an acoustic device that could provide signal variability over time. In addition, the properties of the acoustic field include spatial selectivity - the direction of arrival of the useful signal. There are also spectral features due to the fact that the signal we are considering is broadband. Therefore, parallel use of several detection paths is recommended to increase reliability. In this regard, it is expedient to consider a device that implements the principles of parallel and sequential spectral analysis. The range calculations should be carried out according to the main level of acoustic localization with subsequent correction of the energy range of action with the features of atmospheric distribution of sound velocity. Thus, the above provisions and the general state of development of UAV noise direction finding devices indicate that the proposed work is useful and relevant in a wide range of applications.

The purpose of the article is to expand methods of UAV detection using spectral analysis tools in conditions of broadband interference and signals. The scientific novelty of the proposed work lies in the use of acoustic localization methodology for the task of UAV noise direction finding in conditions of isotropic and anisotropic acoustic interference. As a result of experiments conducted in natural and laboratory conditions, it was determined that the detection path based on mutual spectrum was the most efficient and informative among the detection paths based on intensity, mutual spectrum, and narrowband spectrum. The predicted detection range of UAVs was calculated, which includes both geometric and energetic distances. The structure, operating principle, and display of measurement results in laboratory and natural conditions were implemented using the LabView software package. The results of this work may help in the development of an acoustic air noise localization system that implements the principles of detecting noise-like signals using spectral and correlation methods and can be applied as part of a multisensor complex for detecting and direction finding of UAVs.